This is a presentation on Google's Cloud Platform given by Google Developer Advocate, Chris Schalk on Dec 6th at CloudStock 2010.

1. Introduction to Google’s Cloud Platform
Technologies
Chris Schalk Cloudstock
Google Developer Advocate Monday Dec 6th, 2010

2. What is
cloud
compu/ng?
2

3. Just Kidding ;‐)
3

4. Google Cloud Platform Technologies at Glance
ExisFng Google App Engine
Google App Engine for Business (new)
New! Google
Google BigQuery
Predic/on API
Google Storage

5. Agenda
• Part I - Intro to App Engine
• App Engine Details
• Development Tools
• App Engine for Business
• Part II – Google’s new cloud technologies
• Google Storage
• Prediction API
• BigQuery

6. Part I – Intro to App Engine
Topics covered
• App Engine a PaaS
• App Engine usage/customers
• App Engine Technical Details

7. Google App Engine
Build your own applications in Google's cloud

8. Cloud Computing as Gartner Sees It
SaaS
PaaS
IaaS
Source: Gartner AADI Summit Dec 2009
8

9. Why Google App Engine?
• Easy to build
• Easy to maintain
• Easy to scale
9

10. By the Numbers
500M+
100,000+
250,000+
daily
Apps
Developers
Pageviews
10
10

11. Some App Engine Partners
11

12. App Engine Details
12

13. Cloud Development in a Box
• Downloadable SDK
• Application runtimes
• Java, Python
• Local development tools
• Eclipse plugin, AppEngine Launcher
• Specialized application services
• Cloud based dashboard
• Ready to scale
• Built in fault tolerance, load balancing
13

14. Specialized Services
Memcache Datastore URL Fetch
Mail XMPP Task Queue
Images Blobstore User Service
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15. Language Runtimes
Duke, the Java mascot
Copyright © Sun Microsystems Inc., all rights reserved.
15

16. Ensuring Portability
16

17. Extended Language support through JVM
• Java
• Scala
• JRuby (Ruby)
• Groovy
• Quercus (PHP)
• Rhino (JavaScript) Duke, the Java mascot
Copyright © Sun Microsystems Inc., all rights reserved.
• Jython (Python)
17

18. Always free to get started
• ~5M pageviews/month
• 6.5 CPU hrs/day
• 1 GB storage
• 650K URL Fetch calls/day
• 2,000 recipients emailed
• 1 GB/day bandwidth
• 100,000 tasks enqueued
• 650K XMPP messages/day
18

19. Application Platform Management
19

20. App Engine Dashboard
20

21. App Engine Health History
21

22. Development Tools for App Engine
22

23. Google App Engine Launcher
23

24. SDK Console
24

25. Google Plugin for Eclipse
25

26. Two+ years in review
Apr 2008
Python launch
May 2008
Memcache, Images API
Jul 2008
Logs export
Aug 2008
Batch write/delete
Oct 2008
HTTPS support
Dec 2008
Status dashboard, quota details
Feb 2009
Billing, larger files
Apr 2009
Java launch, DB import, cron support, SDC
May 2009
Key-only queries
Jun 2009
Task queues
Aug 2009
Kindless queries
Sep 2009
XMPP
Oct 2009
Incoming email
Dec 2009
Blobstore
Feb 2010
Datastore cursors, Appstats
Mar 2010
Read policies, IPv6
May 2010
App Engine for Business
Jun 2010
Task queue increases, Python pre-compilation…
Jul 2010
Mapper API
Aug 2010
Multi-tenancy, hi perf img serving, custom err pages
Oct 2010
Instances Console, Delete Kind/App Data
26

27. Introducing App Engine for Business
App Engine for Business
Same scalable cloud platform, but designed for the Enterprise
27

28. Google App Engine for Business Details
• Enterprise application management
– Centralized domain console (preview available)
• Enterprise reliability and support Google App Engine
– 99.9% Service Level Agreement for Business
– Direct support
• Hosted SQL
– Relational SQL database in the cloud (preview available)
• SSL on your domain
• Extremely Secure by default
– Integrated Single Sign On (SSO)
• Pricing that makes sense
– Apps cost $8 per user, up to $1000 max per month
28

29. Enterprise App Development with Google
Buy from others Buy from Google Build your own
Google Apps Google Apps Google App Engine
Marketplace for Business for Business
Enterprise Application Platform
Enterprise Firewall
Enterprise Data AuthenFcaFon Enterprise Services User Management
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30. App Engine for Business
Roadmap
Enterprise Administration
Preview (signups available)
Console
Direct Support Preview (signups available)
Hosted SQL Preview (signups available)
Service Level Agreement Available Q4 2010 (Draft published)
Enterprise billing Available Q4 2010
Custom Domain SSL Limited Release EOY 2010
30

31. App Engine Resources
Get started with App Engine
• http://code.google.com/appengine
Read up on App Engine for Business and become a trusted tester
• http://code.google.com/appengine/business
• bit.ly/gae4btt <- sign up!

32. Part II - Google’s new Cloud Technologies
Topics covered
• Google Storage for Developers
• Prediction API (machine learning)
• BigQuery

33. Google Storage for Developers
Store your data in Google's cloud

34. What Is Google Storage?
• Store your data in Google's cloud
o any format, any amount, any Fme
• You control access to your data
o private, shared, or public
• Access via Google APIs or 3rd party tools/libraries

35. Sample Use Cases
Static content hosting
e.g. static html, images, music, video
Backup and recovery
e.g. personal data, business records
Sharing
e.g. share data with your customers
Data storage for applications
e.g. used as storage backend for Android, AppEngine, Cloud based apps
Storage for Computation
e.g. BigQuery, Prediction API

36. Google Storage Benefits
High Performance and Scalability
Backed by Google infrastructure
Strong Security and Privacy
Control access to your data
Easy to Use
Get started fast with Google & 3rd party tools

37. Google Storage Technical Details
• RESTful API
o Verbs: GET, PUT, POST, HEAD, DELETE
o Resources: identified by URI
o Compatible with S3
• Buckets
o Flat containers
• Objects
o Any type
o Size: 100 GB / object
• Access Control for Google Accounts
o For individuals and groups
• Two Ways to Authenticate Requests
o Sign request using access keys
o Web browser login

38. Performance and Scalability
• Objects of any type and 100 GB / Object
• Unlimited numbers of objects, 1000s of buckets
• All data replicated to multiple US data centers
• Utilizes Google's worldwide network for data delivery
• Only you can use bucket names with your domain names
• Read-your-writes data consistency
• Range Get

39. Demo
• Tools:
o GS Manager
o GSUtil
• Upload / Download

40. Google Storage usage within Google
Google BigQuery Google
Predic/on API
HaiF Relief Imagery USPTO data
Partner ReporFng Partner ReporFng

41. Some Early Google Storage Adopters

42. Google Storage - Pricing
o Storage
 $0.17/GB/Month
o Network
 Upload - $0.10/GB
 Download
 $0.15/GB Americas / EMEA
 $0.30/GB APAC
o Requests
 PUT, POST, LIST - $0.01 / 1000 Requests
 GET, HEAD - $0.01 / 10000 Requests

43. Google Storage - Availability
• Limited preview in US currently
o 100GB free storage and network from Google per
account
o Sign up for waitlist at http://code.google.com/apis/
storage/
• Note: Non US preview available on case-by-case basis

44. Google Prediction API
Google's prediction engine in the cloud

45. Introducing the Google Prediction API
• Google's sophisticated machine learning technology
• Available as an on-demand RESTful HTTP web service

46. How does it work?
"english" The quick brown fox jumped over the lazy
The Prediction API dog.
finds relevant
features in the "english" To err is human, but to really foul things up
sample data during you need a computer.
training. "spanish" No hay mal que por bien no venga.
"spanish" La tercera es la vencida.
The PredicFon API
? To be or not to be, that is the quesFon.
later searches for
those features ? La fe mueve montañas.
during predicFon.

47. A virtually endless number of applicaFons...
Customer TransacFon Species Message DiagnosFcs
Sentiment Risk IdenFficaFon RouFng
Churn Legal Docket Suspicious Work Roster Inappropriate
PredicFon ClassificaFon AcFvity Assignment Content
Recommend PoliFcal Uplij Email Career
Products Bias MarkeFng Filtering Counselling
... and many more ...

48. A PredicFon API Example
AutomaFcally categorize and respond to emails by language
• Customer: ACME Corp, a multinational organization
• Goal: Respond to customer emails in their language
• Data: Many emails, tagged with their languages
• Outcome: Predict language and respond accordingly

49. Using the Prediction API
A simple three step process...
Upload your training data to
1. Upload Google Storage
Build a model from your data
2. Train
3. Predict Make new predicFons

50. Step 1: Upload
Upload your training data to Google Storage
• Training data: outputs and input features
• Data format: comma separated value format
(CSV)
"english","To err is human, but to really ..."
"spanish","No hay mal que por bien no venga."
...
Upload to Google Storage
gsutil cp ${data} gs://yourbucket/${data}

51. Step 2: Train
Create a new model by training on data
To train a model:
POST prediction/v1.1/training?data=mybucket%2Fmydata
Training runs asynchronously. To see if it has finished:
GET prediction/v1.1/training/mybucket%2Fmydata
{"data":{
"data":"mybucket/mydata",
"modelinfo":"estimated accuracy: 0.xx"}}}

52. Step 3: Predict
Apply the trained model to make predicFons on new data
POST prediction/v1.1/query/mybucket%2Fmydata/predict
{ "data":{
"input": { "text" : [
"J'aime X! C'est le meilleur" ]}}}

53. Step 3: Predict
Apply the trained model to make predicFons on new data
POST prediction/v1.1/query/mybucket%2Fmydata/predict
{ "data":{
"input": { "text" : [
"J'aime X! C'est le meilleur" ]}}}
{ data : {
"kind" : "prediction#output",
"outputLabel":"French",
"outputMulti" :[
{"label":"French", "score": x.xx}
{"label":"English", "score": x.xx}
{"label":"Spanish", "score": x.xx}]}}

54. Step 3: Predict
Apply the trained model to make predicFons on new data
An example using Python
import httplib
header = {"Content-Type" : "application/json"}
#...put new data in JSON format in params variable
conn = httplib.HTTPConnection("www.googleapis.com")conn.request("POST",
"/prediction/v1.1/query/mybucket%2Fmydata/predict”, params, header)
print conn.getresponse()

55. Prediction API Capabilities
Data
• Input Features: numeric or unstructured text
• Output: up to hundreds of discrete categories
Training
• Many machine learning techniques
• Automatically selected
• Performed asynchronously
Access from many platforms:
• Web app from Google App Engine
• Apps Script (e.g. from Google Spreadsheet)
• Desktop app

56. Prediction API v1.1 - new features
• Updated Syntax
• Multi-category prediction
o Tag entry with multiple labels
• Continuous Output
o Finer grained prediction rankings based on multiple labels
• Mixed Inputs
o Both numeric and text inputs are now supported
Can combine continuous output with mixed inputs

57. Google BigQuery
Interactive analysis of large datasets in Google's cloud

58. Introducing Google BigQuery
• Google's large data adhoc analysis technology
o Analyze massive amounts of data in seconds
• Simple SQL-like query language
• Flexible access
o REST APIs, JSON-RPC, Google Apps Script

59. Why BigQuery?
Working with large data is a challenge

60. Many Use Cases ...
InteracFve Tools Trends
Spam DetecFon
Web Dashboards Network
OpFmizaFon

61. Key CapabiliFes of BigQuery
• Scalable: Billions of rows
• Fast: Response in seconds
• Simple: Queries in SQL
• Web Service
o REST
o JSON-RPC
o Google App Scripts

62. Using BigQuery
Another simple three step process...
Upload your raw data to
1. Upload Google Storage
Import raw data into BigQuery table
2. Import
3. Query Perform SQL queries on table

63. Writing Queries
Compact subset of SQL
o SELECT ... FROM ...
WHERE ...
GROUP BY ... ORDER BY ...
LIMIT ...;
Common functions
o Math, String, Time, ...
Statistical approximations
o TOP
o COUNT DISTINCT

64. BigQuery via REST
GET /bigquery/v1/tables/{table name}
GET /bigquery/v1/query?q={query}
Sample JSON Reply:
{
"results": {
"fields": { [
{"id":"COUNT(*)","type":"uint64"}, ... ]
},
"rows": [
{"f":[{"v":"2949"}, ...]},
{"f":[{"v":"5387"}, ...]}, ... ]
}
}
Also supports JSON-RPC

65. Security and Privacy
Standard Google Authentication
• Client Login
• OAuth
• AuthSub
HTTPS support
• protects your credentials
• protects your data
Relies on Google Storage to manage access

66. Large Data Analysis Example
Wikimedia Revision History
Wikimedia Revision history data from: hmp://download.wikimedia.org/enwiki/latest/enwiki‐
latest‐pages‐meta‐history.xml.7z

67. Using BigQuery Shell
Python DB API 2.0 + B. Clapper's sqlcmd
http://www.clapper.org/software/python/sqlcmd/

68. BigQuery from a Spreadsheet

69. BigQuery from a Spreadsheet

70. Further info available at:
• Google Storage for Developers
o http://code.google.com/apis/storage
• Prediction API
o http://code.google.com/apis/predict
• BigQuery
o http://code.google.com/apis/bigquery

71. Recap
• Google App Engine
o Google’s PaaS cloud development platform
• Google App Engine for Business
o New enterprise version of App Engine
• Google Storage
o New high speed data storage on Google Cloud
• Prediction API
o New machine learning technology able to predict
outcomes based on sample data
• BigQuery
o New service for Interactive analysis of very large data
sets using SQL

72. Q&A

73. Thank You!
Chris Schalk
Google Developer
Advocate
http://twitter.com/cschalk